Vision-Language Model for Accurate Crater Detection

• URL: http://arxiv.org/abs/2601.07795v1
• Date: Mon, 12 Jan 2026 18:08:17 GMT
• Title: Vision-Language Model for Accurate Crater Detection
• Authors: Patrick Bauer, Marius Schwinning, Florian Renk, Andreas Weinmann, Hichem Snoussi,
• Abstract summary: The European Space Agency (ESA) has a profound interest in reliable crater detection, since craters pose a risk to safe lunar landings.<n>It is non-trivial due to the vast amount of craters of various sizes and shapes, as well as challenging conditions such as varying illumination and rugged terrain.<n>We propose a deep-learning crater detection algorithm based on the OWLv2 model, that has proven highly effective in various computer vision tasks.
• Score: 2.6038033465934083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The European Space Agency (ESA), driven by its ambitions on planned lunar missions with the Argonaut lander, has a profound interest in reliable crater detection, since craters pose a risk to safe lunar landings. This task is usually addressed with automated crater detection algorithms (CDA) based on deep learning techniques. It is non-trivial due to the vast amount of craters of various sizes and shapes, as well as challenging conditions such as varying illumination and rugged terrain. Therefore, we propose a deep-learning CDA based on the OWLv2 model, which is built on a Vision Transformer, that has proven highly effective in various computer vision tasks. For fine-tuning, we utilize a manually labeled dataset fom the IMPACT project, that provides crater annotations on high-resolution Lunar Reconnaissance Orbiter Camera Calibrated Data Record images. We insert trainable parameters using a parameter-efficient fine-tuning strategy with Low-Rank Adaptation, and optimize a combined loss function consisting of Complete Intersection over Union (CIoU) for localization and a contrastive loss for classification. We achieve satisfactory visual results, along with a maximum recall of 94.0% and a maximum precision of 73.1% on a test dataset from IMPACT. Our method achieves reliable crater detection across challenging lunar imaging conditions, paving the way for robust crater analysis in future lunar exploration.

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